rtd-analysis

Analysis of RTD (Denver-area public transit) reliability

Motivation

• A line opened in April to great fanfare
• lots of delays and closures
• gained a reputation for unreliability
• Aurora line hasn't opened because it's operated by same company, want to get issues worked out

Process

1. Collect all tweets from the RTD Twitter account @RideRTD (twitter-collect.py)
2. Filter out irrelevant tweets (post-process.py)
3. Use tweets to generate data on delays by line

• delays or closures
• if a delay is expressed as a range (e.g. "15-30 minutes"), the upper bound was used
• when URLs were given in the tweet they were referenced for additional details
• if there is a scheduled delay that did not occur (according to a subsequent tweet), not recorded

4. Analyze frequency and duration of delays (analysis.py)

• Future questions:
  • assume that every mile of track has a failure rate, where any delay or closure on a day is a failure, and estimate this rate for each line
  • are certain lines correlated (for occurrence and duration)?
    • hypothesis: geographically colocated lines will have correlated delays
    • correlation could also be indicated by an outage reported in the same tweet

Setup

1. Go to https://apps.twitter.com/
2. Sign in
3. Create new app (if necessary)
4. Put the following four values in a dotfile in your project:

• Consumer Key (export TWITTER_CONSUMER_KEY=foo)
• Consumer Secret (export TWITTER_CONSUMER_SECRET=foo)
• Access Token Key (export TWITTER_ACCESS_KEY=foo)
• Access Token Secret (export TWITTER_ACCESS_SECRET=foo)

5. Source the dotfile (and make sure it's in your .gitignore)
6. Install TwitterAPI module: pip install TwitterAPI
7. Modify twitter-collect.py for the username whose tweets you wish to collect

Acknowledgements

• Twitter code adapted from code by Charley Frazier and yanofsky.